Methods and system to compare different options in a decision making process

ABSTRACT

A system, method and computer program product for interfacing a decision engine and a marketing engine in order to provide vendor-related data in response to decision-related data is disclosed. In at least one embodiment, the system and method may include providing a decision engine on a user-accessible network, interfacing a marketing engine with the decision engine on the network; receiving a plurality of user inputs with the decision engine; processing decision-related data with the decision engine in accordance with the plurality of user inputs; sharing the decision-related data with the marketing engine; processing the decision-related data with the marketing engine; and transmitting vendor-related data via the network.

RELATED APPLICATION(S)

This present application is a continuation of U.S. application Ser. No.13/632,277, filed Oct. 1, 2012, which continuation-in-part of U.S.application Ser. No. 12/683,758, filed Jan. 7, 2010, which is acontinuation of both U.S. application Ser. No. 12/081,353, filed Apr.15, 2008, now U.S. Pat. No. 8,051,023, and U.S. application Ser. No.12/081,352, filed Apr. 15, 2008, now U.S. Pat. No. 8,065,261, whichclaim the benefit of U.S. Provisional Application No. 60/935,650, filedon Aug. 23, 2007. The entire teachings of the above applications areincorporated herein by reference.

BACKGROUND

Internet searching is a popular way for internet users to collectinformation about products that they are considering for purchase.Popular web search engines, such as GOOGLE®, YAHOO!® and LIVE SEARCH®(formerly MSN SEARCH®), rely on user-inputted, keyword-based searchqueries in order to provide links to relevant web pages and webdocuments arranged in relevancy-ranked lists. Accordingly, users caninput search queries in an effort to find web pages and web documentsthat focus on the category of products or specific products they wish tolearn about.

Search engines provide users with the ability to educate themselvesabout the products of interest to the extent such information is deemedrelevant to the user-inputted search queries and is available. Thus,search engines often aid users in making informed decisions regardingpurchasing products of interest. Nevertheless, the actual comparison ofthe products and decision-making processes are left to the users as thisis beyond the intended purposes of a search engine.

Popular search engine firms rely on search advertising as a major sourceof income. The fact that internet users often use search engines with aneye towards purchasing products makes them particularly appealing toadvertisers who are attempting to reach their target consumer audience.Since popular internet search engines rely on keywords in providingresults, it follows that search advertising is also sold and deliveredon the basis of keywords.

Popular search engine firms conduct running auctions to sell advertisingspace according to the bids received for keywords. Higher demandkeywords command higher bid prices. Typically, advertisers are chargedbased on click-throughs and not merely the display of theiradvertisements in response to the keywords. Popular search engines thustypically position advertisements on the search result pages based, atleast in part, on click-through rates (“CTRs”).

SUMMARY

In one aspect, a computer-implemented method of providing vendor-relateddata in response to decision-related data is provided. The method caninclude providing a decision engine on a user-accessible network;interfacing a marketing engine with the decision engine on the network;receiving a plurality of user inputs with the decision engine;processing decision-related data with the decision engine in accordancewith the plurality of user inputs; sharing the decision-related datawith the marketing engine; and transmitting vendor-related data via thenetwork.

In another aspect, a computer program product having a computer storagemedium and a computer program mechanism embedded in the computer storagemedium for causing a computer to interface a decision engine and amarketing engine is provided. The computer program mechanism can includea first computer code device configured to interface with the decisionengine; a second computer code device configured to interface with themarketing engine; and a third computer code device configured tofacilitate data sharing between the decision engine and the marketingengine.

In yet another aspect, a system for providing vendor-related data inresponse to decision-related data is provided. The system can includeone or more servers on a network; a decision engine provided on the oneor more servers, the decision engine connected to one or more storagedevices for storing, at least in part, decision-related data; amarketing engine provided on the one or more servers, the marketingengine connected to the one or more storage devices for storing, atleast in part, vendor-related data; an interfacing application tying themarketing engine to the decision engine, the interfacing application onthe one or more servers, wherein the interfacing application is capableof facilitating data sharing between the decision engine and themarketing engine; and wherein the decision engine is capable of servinga plurality of client computing devices on a user-accessible portion ofthe network.

An embodiment of the present disclosure is a computer-implemented methodfor providing vendor-related data. The method comprises receivingmodified decision-related data from a decision engine. The modifieddecision-related data is generated by the decision engine by applying atleast one user input to decision-related data, wherein thedecision-related data is at least one factor used as a criteria inselecting at least one decision option. The at least one decision optionis an output of the decision engine in response to the application ofthe at least one user input to the decision-related data.

In addition, the embodiment retrieves vendor-related data from amarketing database based on one or more properties of the modifieddecision-related data. The retrieving includes matching the at least onefactor to at least one vendor factor associated with the vendor-relateddata and obtaining vendor-related data associated with at least a subsetof the at least one vendor factor. The at least one vendor factor is acriteria used in selecting the vendor-related data.

Also, the embodiment processes the modified decision-related data tomodify the at least one vendor factor and selects a subset of thevendor-related data based on the modified at least one vendor factor.

The embodiment may provide the selected subset of vendor-related data tothe user. The embodiment may also trigger a marketing engine associatedwith the marketing database to provide the selected subset ofvendor-related data to the user. Additionally, the embodiment mayprovide the selected subset of vendor-related data to a marketing engineassociated with the marketing database as a recommendation ofvendor-related data to provide to the user. The marketing engineutilizes the recommendation to provide vendor-related data to the user.

The modified decision-related data may include at least one factorhaving a weighted score, where the weighted score of the at least onefactor is determined by at least an importance assigned to the at leastone factor by the user.

The embodiment may retrieve the vendor-related data by selecting themarketing database corresponding to a vendor and retrieving thevendor-related data from the selected at least one marketing database.

The embodiment may select the marketing database corresponding to thevendor by selecting the vendor based on whether or not goods and/orservices provided by the vendor correspond to the decision-related data.

Also, the embodiment may process the modified decision-related data byranking the vendor-related data by applying weighted scores associatedwith the decision-related data to the vendor factors and selecting asubset of the ranked vendor-related data. Further, the embodiment mayselect the subset of ranked vendor-related data by selecting the subsetbased on an advertisement subscription of a vendor associated with thevendor-related data. In addition, the embodiment may provide thevendor-related data to the user by presenting the vendor-related dataalong with at least one decision option. The at least one decisionoption is presented to the user by the decision engine utilizing themodified decision-related data. The at least one decision option may beproduct and/or service related data.

Another embodiment of the present disclosure is a system for providingvendor-related data. The system comprises one or more processors on anetwork. The system also comprises an interface, provided on the one ormore processors, configured to receive modified decision-related datafrom a decision engine. The modified decision-related data is generatedby the decision engine by applying at least one user input todecision-related data. The decision-related data is at least one factorused as a criteria in selecting at least one decision option. The atleast one decision option is an output of the decision engine inresponse to the application of the at least one user input to thedecision-related data.

The system also comprises a retrieving module, provided on the one ormore processors, configured to retrieve vendor-related data from amarketing database based on one or more properties of the modifieddecision-related data. The retrieving module is further configured tomatch the at least one factor to at least one vendor factor associatedwith the vendor-related data and obtain vendor-related data associatedwith at least a subset of the at least one vendor factor. The at leastone vendor factor is a criteria used in selecting the vendor-relateddata.

In addition, the system comprises a selection module, provided on theone or more processors, configured to process the modifieddecision-related data to modify the at least one vendor factor. Theselection module is further configured to select a subset of thevendor-related data based on the modified at least one vendor factor.

The interface may be further configured to provide the selected subsetof vendor-related data to the user. The interface may also be furtherconfigured to trigger a marketing engine associated with the marketingdatabase to provide the selected subset of vendor-related data to theuser. Also, the interface may be further configured to provide theselected subset of vendor-related data to a marketing engine associatedwith the marketing database as a recommendation of vendor-related datato provide to the user, the marketing engine utilizing therecommendation to provide vendor-related data to the user.

The modified decision-related data includes at least one factor having aweighted score, the weighted score of the at least one factor determinedby at least an importance assigned to the at least one factor by theuser.

The retrieving module may be further configured to select the marketingdatabase corresponding to a vendor and retrieve the vendor-related datafrom the selected at least one marketing database. The retrieving modulemay also be further configured to select the vendor based on whether ornot goods and/or services provided by the vendor correspond to thedecision-related data.

The selecting module may include a ranking module configured to rank thevendor-related data by applying weighted scores associated with thedecision-related data to the vendor factors. The selecting module may befurther configured to select a subset of the ranked vendor-related data.Also, the selecting module may be further configured to select thesubset of the ranked vendor-related data based on an advertisementsubscription of a vendor associated with the vendor-related data.

The system may further comprise a presenting module configured toprovide the vendor-related data along with at least one decision option.The at least one decision option is presented to the user by thedecision engine utilizing the modified decision-related data. The atleast one decision option may be product and/or service related data.

Yet another example embodiment is a computer-implemented method forproviding vendor-related data. The method comprises receiving modifieddecision-related data from a decision engine. The modifieddecision-related data is generated by the decision engine by applying atleast one user input to decision related data. The method also comprisesretrieving vendor-related data from a marketing database based on one ormore properties of the modified decision-related data. Also, the methodcomprises processing the modified decision-related data to modify atleast one vendor factor associated with the retrieved vendor-relateddata and selecting a subset of the retrieved vendor-related data basedon the modified at least one vendor factor.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages will beapparent from the following more particular description of theembodiments, as illustrated in the accompanying drawings in which likereference characters refer to the same parts throughout the differentviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating the principles of the embodiments.

FIG. 1 is a schematic illustration of a decision making and responsesystem.

FIG. 2 is an enlarged schematic illustration of the system shown in FIG.1.

FIG. 3 is an illustration of a factors data entry page that may be usedwith the system shown in FIG. 1.

FIG. 4 is an illustration of a graph factors page that may be used withthe system shown in FIG. 1.

FIG. 5 is an illustration of a decision options page that may be usedwith the system shown in FIG. 1.

FIG. 6 is an illustration of a raw data page that may be used with thesystem shown in FIG. 1.

FIG. 7 is an illustration of a score results page that may be used withthe system shown in FIG. 1.

FIG. 8 is an illustration of a graph results page that may be used withthe system shown in FIG. 1.

FIG. 9 is an illustration of a page including a pop-up summary windowthat may be used with the system shown in FIG. 1.

FIG. 10 is a flowchart of a method of comparing different options usingthe system shown in FIG. 1.

FIG. 11 is an illustration of a marketing profile data entry page thatmay be used with the system shown in FIG. 1.

FIG. 12 is an illustration of another marketing profile data entry pagethat may be used with the system shown in FIG. 1.

FIG. 13 is a flowchart of a method of providing vendor-related data inresponse to decision-related data using the system shown in FIG. 1.

FIG. 14 is a block diagram of decision and response engines that may beprovided to the system shown in FIG. 1.

FIG. 15 is an exemplary diagram of a unique set of factors associatedwith a user.

FIG. 16 is another exemplary diagram of a unique set of factorsassociated with a user.

FIG. 17 is an exemplary diagram of a base set of factors of a user.

FIG. 18 is an exemplary diagram of a score based upon factors of a user.

FIG. 19 is another exemplary diagram of a score based upon factors of auser.

FIG. 20 is another exemplary diagram of a score based upon factors of auser.

FIG. 21 is another exemplary diagram of a score based upon factors of auser.

FIG. 22 is network diagram illustrating a connector engine used toconnect a decision engine to a marketing engine in accordance with anexample embodiment of the present disclosure.

FIG. 23 is a block diagram of a connector engine in accordance with anexample embodiment of the present disclosure.

FIGS. 24A-D are tables illustrating decision logic utilized by aconnector engine in accordance with an example embodiment of the presentdisclosure.

FIG. 25 is a flow diagram of an example method for providingvendor-related data.

DETAILED DESCRIPTION

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein. Scope of the invention is thus indicated bythe appended claims, rather than by the foregoing description, and allchanges that come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

Aspects of the present invention are disclosed in the followingdescription and related figures directed to specific embodiments of theinvention. Those skilled in the art will recognize that alternateembodiments may be devised without departing from the spirit or thescope of the claims. Additionally, well-known elements of exemplaryembodiments of the invention will not be described in detail or will beomitted so as not to obscure the relevant details of the invention.

As used herein, the word “exemplary” means “serving as an example,instance or illustration.” The embodiments described herein are notlimiting, but rather are exemplary only. It should be understood thatthe described embodiments are not necessarily to be construed aspreferred or advantageous over other embodiments. Moreover, the terms“embodiments of the invention”, “embodiments” or “invention” do notrequire that all embodiments of the invention include the discussedfeature, advantage or mode of operation.

Further, many of the embodiments described herein are described in termsof sequences of actions to be performed by, for example, elements of acomputing device. It should be recognized by those skilled in the artthat the various sequence of actions described herein can be performedby specific circuits (e.g., application specific integrated circuits(ASICs)) and/or by program instructions executed by at least oneprocessor. Additionally, the sequence of actions described herein can beembodied entirely within any form of computer-readable storage mediumsuch that execution of the sequence of actions enables the processor toperform the functionality described herein. Thus, the various aspects ofthe present invention may be embodied in a number of different forms,all of which have been contemplated to be within the scope of theclaimed subject matter. In addition, for each of the embodimentsdescribed herein, the corresponding form of any such embodiments may bedescribed herein as, for example, “a computer configured to” perform thedescribed action.

FIG. 1 is a schematic illustration of a decision making and responsesystem 100. FIG. 2 is an enlarged schematic illustration of system 100.In the exemplary embodiment, system 100 may include a web portal 102, atleast one first user 104 and at least one second user 106. In theexemplary embodiment, each first user 104 and each second user 106 maybe coupled in communication to web portal 102 using a network 110. Inthe exemplary embodiment, network 110 may be the Internet. In analternative embodiment, network 110 may be a local area network (LAN), awireless LAN, a wide area network (WAN) and/or any other type ofconnection that enables system 100 to function as described herein.System 100, in the exemplary embodiment, may also include at least oneserver 112 and at least one database 114. In the exemplary embodiment,server 112 may be coupled in communication to database 114 using anetwork connection 115 that is coupled to a local network 116 such as,but not limited to, a LAN, a wireless LAN, a WAN and/or any otherconnection that enables system 100 to function as described herein.

In the exemplary embodiment, first users 104 may be any entity thatdesires to make a decision on a plurality of decision options using atleast one factor. Each factor represents a specific consideration that auser may take into account when selecting the decision option. System100 may be used with decision analyses including, but not limited to,the purchase of any type of products or services, the purchase of anytype of real estate, determining which school to attend, determiningwhich career path to pursue or any other decision. In one embodiment,first users 104 may be the general public. Second users 106 may be anyentity that is interested in the decision analysis conducted by firstusers 104. In one embodiment, second users 106 may be any entity thatdesires to advertise decision options to first users 104. In anotherembodiment, second users 106 may include, but not limited to,advertising agencies, advertisers and specific product entities.

In the exemplary embodiment, server 112 may include at least one engine118 programmed therein. Alternatively, a plurality of servers 112 may beused, wherein each server 112 may include at least one engine 118programmed therein. As used herein, the term “engine” may refer to acollection of logic and/or code that may be executed on server 112 orany other type of device or processor that is capable of producing aresponse. In the exemplary embodiment, first engine 118 may be adecision engine. In one embodiment, server 112 may include a secondengine 120 that may be a connector engine and a third engine 122 thatmay be a response engine, such as an advertising/marketing engine. Inanother embodiment, server 112 may include any type of engines or anynumber of engines that enable system 100 to function as describedherein.

In the exemplary embodiment, the term “server” is not limited to justthose integrated circuits referred to in the an as a computer, butbroadly refers to a processor, a microcontroller, a microcomputer, aprogrammable logic controller, an application specific integratedcircuit and other programmable circuits. These aforementioned terms maybe used interchangeably herein. In the exemplary embodiment, server 112may include a bus 130 or other communication mechanism for communicatinginformation, and a processor 132 coupled with bus 130 for processing theinformation. In one embodiment, a plurality of processors 132 may bearranged in a multi-processor arrangement to facilitate fasterprocessing as compared to a single processor arrangement. In theexemplary embodiment, system 100 may also include a main memory 134,such as a random access memory (RAM) or other dynamic storage device(e.g., dynamic RAM (DRAM), static RAM (SRAM) and synchronous DRAM(SDRAM)) coupled to bus 130 for storing information and instructions tobe executed by processor 132. In addition, main memory 134 may be usedfor storing temporary variables or other intermediate information duringthe execution of instructions by processor 132. System 100 may furtherinclude a read only memory (ROM) 136 or other static storage device(e.g., programmable ROM (PROM), erasable PROM (EPROM) and electricallyerasable PROM (EEPROM)) coupled to bus 130 for storing staticinformation and instructions for processor 132.

System 100 may also include a disk controller 138 coupled to bus 130 tocontrol one or more storage devices for storing information andinstructions. In the exemplary embodiment, storage devices may include,but not limited to, a magnetic hard disk 140 and a removable media drive142 (e.g., floppy disk drive, read-only compact disc drive, read/writecompact disc drive, compact disc jukebox, tape drive and removablemagneto-optical drive). The storage devices may be coupled to system 100using any appropriate device interface known to one having ordinaryskill in the art (e.g., small computer system interface (SCSI),integrated device electronics (IDE), enhanced-IDE (E-IDE), direct memoryaccess (DMA), or ultra-DMA). System 100 may also include special purposelogic devices (e.g., application specific integrated circuits (ASICs))or configurable logic devices (e.g., simple programmable logic devices(SPLDs), complex programmable logic devices (CPLDs), and fieldprogrammable gate arrays (FPGAs)).

In the exemplary embodiment, main memory 134, hard disk 140 andremovable media drive 142 are examples of computer-readable mediums thatfacilitate holding instructions programmed according to the teachings ofthe invention, data structures, tables, records and/or other datadescribed herein. The term “computer-readable medium” or“computer-readable media” as used herein refers to any medium thatfacilitates storing and/or providing instructions to processor 132 forthe execution thereof. The computer-readable media may include, but notlimited to, non-volatile media, volatile media and transmission media.Non-volatile media may include, but not limited to, hard disks, floppydisks, tape, magneto-optical disks, PROMs (EPROM, EEPROM, flash EPROM),DRAM, SRAM, SDRAM or any other magnetic medium. Moreover, non-volatilemedia may also include compact discs (e.g., CD-ROM) or any other opticalmedium. Further, non-volatile media may include punch cards, paper tapeor other physical medium with patterns of holes. Volatile media mayinclude dynamic memory, such as main memory 134. Transmission media mayinclude coaxial cables, copper wire and fiber optics, including thewires that make up bus 130. Transmission media may also include carrierwaves such as acoustic or light waves that may be generated using radiowaves and infrared data communications.

In the exemplary embodiment, the computer-readable media may includesoftware that facilitates controlling system 100. Such software mayinclude, but is not limited to, device drivers, operating systems,development tools and applications software. Such computer-readablemedia further includes the computer program product of the presentinvention for performing all or a portion (if processing is distributed)of the processing performed in implementing the invention.

The computer code devices of the present invention may be anyinterpretable or executable code mechanism, including but not limited toscripts, interpretable programs, dynamic link libraries (DLLs), Javaclasses, and complete executable programs. Moreover, parts of theprocessing of the present invention may be distributed for betterperformance, reliability, and/or cost.

In the exemplary embodiment, system 100 may further include a displaycontroller 144 coupled to bus 130 to control a display 146, such as acathode ray tube (CRT), liquid crystal display (LCD) or any other typeof display to facilitate displaying information to a computer user.System 100 may include a plurality of input devices, such as a keyboard148 and a pointing device 150, to facilitate interacting with thecomputer user and providing information to processor 132. Alternatively,a touch screen may be used in conjunction with display 146. In oneembodiment, pointing device 150 may be a mouse, a trackball or apointing stick for communicating direction information and commandselections to processor 132 and for controlling cursor movement ondisplay 146. In addition, a printer (not shown) may be coupled to system100 to facilitate printing data stored and/or generated by system 100.

System 100 also includes a communication interface 152 coupled to bus130, wherein communication interface 152 may be coupled in communicationto LAN 116 or network 110 using network connection 115. In oneembodiment, communication interface 152 may be a network interface cardthat is coupled in communication to any packet switched LAN. In anotherembodiment, communication interface 152 may be an asymmetrical digitalsubscriber line (ADSL) card, an integrated services digital network(ISDN) card or a modem to facilitate providing a data communicationconnection to network connection 115. In yet another embodiment,wireless connections may be used to couple communication interface 152to LAN 116 and/or network 110. In the exemplary embodiment,communication interface 152 sends and receives electrical,electromagnetic or optical signals that carry digital data to and fromsystem 100, which are exemplary forms of carrier waves that facilitatetransporting information. Network connection 115 facilitates providingdata communication between web portal 102 and data devices usingnetworks 116 and 130. Specifically, network connection 115 may couplefirst users 104 and/or second users 106 to web portal 102 using at leastone of local network 116 and network 110. System 100 may also transmitand receive data, including program code, through networks 116 and 110using network connection 115 and communication interface 152. Moreover,network connection 115 may couple server 112 in communication to amobile device 132 such as a personal digital assistant (PDA), a laptopcomputer, a cellular telephone, a smart phone, an ultra-compact mobiledevice or any other mobile device that enables system 100 to function asdescribed herein.

During operation, system 100 may perform a portion or all of theprocessing steps of the invention in response to processor 132 executingone or more sequences of one or more instructions contained within mainmemory 134 and/or other forms of computer-readable media. In oneembodiment, processor 132 may execute the instructions contained withinthe computer-readable media. In another embodiment, hard-wired circuitrymay be used in place of or in combination with the instructions. Thus,the exemplary embodiments described herein are not limited to anyspecific combination of hardware circuitry and software. For example,the instructions may initially be carried on a magnetic disk of a remotecomputer. The remote computer can load the instructions for implementingall or a portion of the present invention remotely into a dynamic memoryand send the instructions over a telephone line using a modern. A modernlocal to system 100 may receive the data on the telephone line and usean infrared transmitter to convert the data to an infrared signal. Aninfrared detector coupled to bus 130 can receive the data carried in theinfrared signal and place the data on bus 130. Bus 130 carries the datato main memory 134, from which processor 132 retrieves and executes theinstructions. The instructions received by main memory 134 mayoptionally be stored on hard disk 140 or removable media drive 142either before or after execution by processor 132.

Other aspects of the invention may include data transmission andInternet-related activities. See Gralla, Preston, How the InternetWorks, Ziff-Davis Press (1996), which is hereby incorporated byreference into this patent application. Still other aspects of theinvention may utilize wireless data transmission, such as thosedescribed in U.S. Pat. Nos. 6,456,645, 5,818,328 and/or 6,208,445, allof which are hereby incorporated by reference into this patentapplication.

FIG. 3 is an illustration of a factors data entry interface page, orfactors page 200 that may be used with engine 118. It should beunderstood that the following description is a non-limiting examplepertaining to an exemplary decision engine. In the exemplary embodiment,factors page 200 includes a navigation area 202 and a factor data area204. Navigation area 202 includes a plurality of radio buttons such as,but not limited to, a factors button 206, an options button 208, a databutton 210, a results button 212, a graph results button 214, a graphfactors button 216, a clear button 218 and a summary button 220. Eachradio button facilitates directing a user to the respective interfacepage. In the exemplary embodiment, clear button 218 facilitates hiding ascore summary pop-up window 294 (shown in FIG. 9) and summary button 220facilitates displaying score summary pop-up window 294 on the page.Moreover, navigation area 202 may include a save/load button 222 and anew decisions button 224. In the exemplary embodiment, save/load button222 enables the user to either save their decision analysis progress orload a previously saved decision analysis. Additional information may beincluded when the user saves their results, such as the ability tochoose a destination to save a file to, the ability to name the file tobe saved and the ability to include a description of the results.Additionally, engine 118 may allow the user to load saved decisionanalyses as well as delete prior saved results. Moreover, new decisionsbutton 224 enables the user to begin a new decision analysis.

Factor data area 204 may include a plurality of data columns 226 and atleast one data row 228. The plurality of data columns 226 may include,but not limited to, a factors column 232, a first data column 234 and asecond data column 236, wherein each column 232, 234 and 236 includes atleast one data entry field 238. Each data row 228 may include an ON/OFFtoggle button 244, an importance indicator 246 and at least one dataentry field 238 associated with columns 232, 234 and 236. In theexemplary embodiment, each ON/OFF button 244 facilitates including orexcluding the associated data row 228 from the decision analysisperformed by engine 118. Moreover, each importance indicator 246facilitates indicating the importance of the associated factor that isassigned by the user. In the exemplary embodiment, an aspect of eachfactor may be indicated in at least one of first and second data columns234 and 236. Specifically, in one embodiment, an aspect of each factormay be indicated with a range of values. In another embodiment, anaspect of each factor may be indicated as a subjective assessment. Inyet another embodiment, an aspect of each factor may be indicated as atleast one of a yes/no answer, a true/false answer, a multiple-choiceanswer, numerical data and any other type of entry known by one havingordinary skill in the art. For example, in the event that each factormay be quantified, a most desired value may be entered into first datacolumn 234 and a least desired value may be entered into second datacolumn 236. In the event that each factor may be represented as asubjective assessment, the user may enter the word “opinion” in at leastone of first and second columns 234 and 236. In the event that eachfactor may be represented as yes/no or true/false entry, the user mayindicate the most desired entry in first data column 234 and the leastdesired entry in second data column 236.

FIG. 4 is an illustration of a graph factors page 250. In the exemplaryembodiment, the user may navigate to graph factors page 250 by clickinggraph factors radio button 216 in navigation area 202 included in graphfactors page 250. Graph factors page 250 may also include a display area252 that may include a chart 254 that graphically represents theimportance of each factor assigned by the user using importanceindicator 246. Specifically, chart 254 includes a y-axis 256 thatincludes each factor used in the decision process and an x-axis 258 thatincludes the relative importance of each factor. At least one graph bar260 is associated with each factor such that graph bar 260 maygraphically represent the importance of each factor. Moreover, therelative importance of each factor is displayed by importance percentagebasis. In an alternative embodiment, display area 252 may display thefactor importance as a pie chart, a list or any other method ofdisplaying information known to one having ordinary skill in the art toenable engine 118 to function as described herein.

FIG. 5 is an illustration of a decision options page 252. In theexemplary embodiment, the user may navigate to decision options page 252by clicking options radio button 208 in navigation area 202 included indecision options page 252. Decision options page 252 may also include adecision options area 254 that may include at least one data row 256that may include a decision option 258 and at least one ON/OFF togglebutton 244 associated therewith. In the exemplary embodiment, anddescribed in more detail below, engine 118 may determine at least onedecision option 258 based on the factors entered by the user in factorspage 200. In one embodiment, the user may indicate a broad category ofthe desired decision options 258 to which each factor may apply. Inanother embodiment, engine 118 may automatically determine the decisionoption 258 category that applies based on the factors entered by theuser in factors page 200. In yet another embodiment, the user may entera plurality of decision options 258 and at least one factor into system100 to enable engine 118 to decide on an appropriate decision option 258based on the entered decision options 258 and factors.

FIG. 6 is an illustration of a raw data page 260. In the exemplaryembodiment, the user may navigate to data page 260 by clicking dataradio button 210 in navigation area 202 included within data page 260.Data page 260 may also include a data display area 262 that may includeat least one data matrix 264. Specifically, data matrix 264 may includeat least one data column 266 associated with each decision option 258determined by engine 118. Moreover, data matrix 264 may include at leastone data row 268 associated with each factor entered by the user. In theexemplary embodiment, as described in more detail below, engine 118determines the objective score or rating for each factor for eachdecision option 258 and displays the values in matrix form. In theexemplary embodiment, each factor score is non-weighted. As a result,data page 260 displays the raw data to the user. For example, asdescribed in more detail below, in the event the factor score may bequantified, a numeric value is displayed. In the event the factor valueis an opinion, the value may be displayed as a subjective assessmentusing a percent scale. For example, a subjective assessment may bedisplayed as 8 stars out of 10 stars, as described in more detail below.Further, in the event the factor value may be displayed as a yes/noanswer or true/false answer, such an answer is displayed. Moreover, inthe event the factor score is a multiple choice answer, a single answerchoice that applies to the decision option is displayed. For example, ifthe factor is a color, then the factor score may be indicated as thespecific color of the specific decision option.

FIG. 7 is an illustration of a score results page 270. In the exemplaryembodiment, the user may navigate to results page 270 by clickingresults radio button 212 in navigation area 202 included within resultspage 270. Results page 270 may also include a data display area 272 thatmay include at least one data matrix 274. Specifically, data matrix 274may include at least one data column 276 associated with each decisionoption 258 determined by engine 118. Moreover, data matrix 264 may alsoinclude at least one data row 278 associated with each factor entered bythe user. In the exemplary embodiment, as described in more detailbelow, engine 118 determines the weighted score of each factor using thenon-weighted factor score and the importance assigned to the factor bythe user. Each weighted factor score for each decision option 258 isdisplayed in matrix form. In the exemplary embodiment, each weightedfactor score is a numeric number that is used to determine a total scorefor each decision option 258.

FIG. 8 is an illustration of a graph results page 280. In the exemplaryembodiment, the user may navigate to graph results page 280 by clickinggraph results radio button 214 in navigation area 202 included in graphresults page 280. Graph results page 280 may also include a display area282 that may include a chart 284 that graphically represents the scoreof each decision option 258 determined by engine 118. Specifically,chart 284 includes a y-axis 286 that includes at least one decisionoption 258 compared by engine 118 and an x-axis 288 that includes thetotal scores of each decision option 258. A graph bar 290 is associatedwith each decision option 258 such that graph bar 290 may graphicallyrepresent the total score each decision option 258. In an alternativeembodiment, display area 282 may display the total score as a pie chart,a list or any other method of displaying information known to one havingordinary skill in the art to enable system 100 to function as describedherein.

In the exemplary embodiment, once the score for each decision option 258is determined by engine 118, as shown in FIG. 9, the user may click onsummary button 220 to display score summary pop-up window 294 on anypage. Score summary pop-up window 294 may include a list of the compareddecision options 258 and the scores associated with each decision option258.

FIG. 10 is a flow chart of exemplary method 300 of comparing differentoptions using system 100. During operation, a user desiring to decide ona decision option may utilize system 100, and more specifically engine118, to facilitate determining which decision option to choose. In oneembodiment, system 100 may be utilized or implemented on network 110,such as, but not limited to, the Internet. Engine 118 may be used withdecision analyses including, but not limited to, the purchase of anytype of products or services, the purchase of any type of real estate,deciding which school to attend, deciding which career to pursue or anyother type of decision analysis. In the exemplary embodiment, the usermay begin a new decision analysis by clicking new decisions button 224in any of the pages shown in FIGS. 3-9. In method step 302, the user maythen navigate to factors page 200, as shown in FIG. 3, by clickingfactors radio button 206, wherein the user may enter a plurality offactors, corresponding to various aspects of the desired decisionoption, into factors column 232. Moreover, the user may enter specificdata for each factor into first data column 234 and/or second datacolumn 236. The factor data may be quantified by engine 118 according toan importance level assigned to each factor by the user using importanceindicator 246. In method step 304, once the user has entered theplurality of factors, the factor data and assigned the importance levelof each factor, engine 118 may determine a plurality of decision options258 to be analyzed and compared in the decision analysis, as shown inFIG. 4.

Although engine 118 may be used with any decision analysis, anon-limiting example pertaining to deciding which automobile the usershould purchase is used to describe the operation of engine 118. In thisnon-limiting example, engine 118 may analyze and compare various aspectsof automobiles to enable the user to make a decision on which automobileto purchase. Specifically, the plurality of factors may representvarious aspects of automobiles that the user may use to analyze andcompare potential automobiles. For example, factors 1-10 shown in FIG. 3may represent, miles per gallon (MPG), cost, miles per tank, interiornoise level, appearance, quarter mile time, 0-60 miles per hour (MPH)time, turning radius, anti-lock braking system (ABS) and exterior color,respectively. Once the user enters the plurality of automobile factorsinto factors column 232 in factors page 200, the user may enter uniquefactor data for each factor in at least one of first and second columns234 and 236.

In the non-limiting vehicle purchasing example, the unique factor datamay represent aspects of a potentially acceptable automobile that theuser would purchase. In the exemplary embodiment, at least one factormay be represented as a range. For example, the user may be interestedin an automobile that can travel between a range of about 10 to about 35miles per gallon and cost between a range of about $15,000 to about$25,000. Moreover, at least one other factor may be represented as asubjective assessment, a yes/no answer, a true/false answer or amultiple-choice answer. In one embodiment, an automobile factor that maybe a subjective assessment may include interior noise level orappearance. In such an embodiment, the user may enter “opinion” in atleast one of first and second data columns 234 and 236 for the interiornoise level factor. In another embodiment, a factor that may include ayes/no or true/false answer may be whether the automobile has anti-lockbrakes installed. In such an embodiment, in the event the user desiresan automobile that includes anti-lock brakes, the user may enter “yes”as the best answer in first data column 234 and “no” as the worst answerin second data column 236. In yet another embodiment, a factor that mayinclude a multiple-choice answer may be the color of the automobile. Insuch an embodiment, the user may enter “red, green and blue” as the mostdesired colors in first data column 234 and “white, black and yellow” asthe least desired colors in second data column 236. Lastly, once theuser has entered the unique factor data for each factor, the user mayassign the importance level to each factor using importance indicator246. After the importance level of each factor have been assigned, theuser may view a graphical representation of the importance levels ofeach factor by navigating to graph factors page 250 by clicking thegraph factors radio button 216 on any page shown in FIGS. 3-9.

Once the user has entered the plurality of factors, entered the factordata and assigned the importance level to each factor in factors page200, engine 118 may determine a plurality of decision options and outputthose options in decision options page 252, for example as shown in FIG.5. In one embodiment, information for a plurality of decision options,such as but not limited to products, may be located on the Internet. Inone embodiment, engine 118 may search the Internet or any other networkor database for decision option information, which has been tagged andtherefore searchable using an Internet search engine or any other tool.In the exemplary embodiment, ten decision options 258 may be returnedand displayed in decision options page 252. Alternatively, any number ofdecision options 258 may be returned. In the non-limiting vehiclepurchasing decision example, decision options A-J may representdifferent makes and/or models of vehicles that engine 118 determinedwere relevant to the decision analysis. In the exemplary embodiment,each determined decision option 258 is displayed on a corresponding datarow 256 that includes ON/OFF button 244 associated therewith. The usermay include the determined decision option 258 in the decision analysisby switching ON/OFF 244 button to “ON” or exclude the determineddecision option 258 by switching ON/OFF 244 button to “OFF”. In theexemplary embodiment, all ON/OFF buttons 244 are switched to “ON” whichmeans that all of decision options A-J will be analyzed and compared inthe decision analysis. Alternatively, the user may manually provide theplurality of decision options 258 to engine 118.

Once the plurality of decision options 258 have been determined byengine 118, the factor data for each decision option 258 may bedisplayed in data page 260, as shown in FIG. 6. In the non-limitingvehicle purchasing decision example, the aspects of each automobile aredisplayed in data page 260. For example, decision option A, orautomobile A, gets 42 MPG, costs $24,777, gets 600 miles per tank,received 7 out of 8 stars for interior noise level and has an exteriorcolor of red.

Referring back to FIG. 10, in method step 306, system 100, and morespecifically engine 118 may calculate a numeric value, or raw value,that represents the overall desirability of each factor. Specifically,engine 118 may calculate a numeric value of the data entered in firstdata column 234 that represents a best or most desirable value. Further,engine 118 may calculate a numeric value of the factor data entered insecond data column 236 that represents a worst or least desirable value.For example, engine 118 may calculate a numeric value of 10, or anyother value that represents the best or most desirable value, forfactors that include data that is equal to or greater than the dataentered in first data column 234. Similarly, engine 118 may calculate anumeric value of 0, or any other value that represents the worst orleast desirable value, for factors that include data that is equal to orless than the data entered in second data column 236. Moreover, engine118 may calculate a corresponding numeric value for factors that includedata that is between the best and worst values entered in first andsecond data columns 234 and 236 using an algorithm such as linearcomputation. In the non-limiting vehicle purchasing decision example,for factor 1, or MPG, the user entered a most desirable MPG of 35 and aleast desirable MPG of 10. In such an example, engine 118 may calculatea numeric value of 5 for a decision option having an MPG of 22.5, anumeric value of 2.5 for a decision option having an MPG of 16.25, anumeric value of 7.5 for a decision option having an MPG of 28.75 and soforth and so on. Alternatively, factor data that falls between the bestand worst desired values entered in first and second data columns 234and 236 may have numeric values assigned by engine 118 using polynomialcomputation, logarithmic computation, power computation, exponentialcomputation, moving average computation or any other computation methodthat enables engine 118 to function as described herein.

In the non-limiting vehicle purchasing decision example, for factor 4,or interior noise level, the user entered “opinion” in at least one offirst and second data column 234 and 236, shown in FIG. 3. In the eventthe decision option 258 includes a subjective assessment or review froma third party that assessment may be used to calculate a numeric value.For example, in one embodiment, the third party automobile review agencymay determine that a particular vehicle has a relatively low amount ofinterior noise and therefore award 7 stars to that vehicle for interiornoise. In such an example, engine 118 may calculate the rating percentand convert that percent to the numeric number. For example, 7 out of 10stars would equal a 70% interior noise rating. Engine 118 may thencalculate the interior noise factor as 7. In the event the decisionoption 258 does not include a subjective assessment for a third partythat may be used to calculate a numeric value, engine 118 may inform theuser that no numeric value was calculated for that particular factor.

In the non-limiting vehicle purchasing decision example, for factor 9,or ABS, the user entered “yes”, or “true”, in first data column 234 and“no”, or “false”, in second data column 236. In such an example, in theevent a vehicle includes ABS, the engine 118 may calculate a numericvalue of 10 for factor 9. In the event a vehicle does not include ABS,then engine 118 may calculate a numeric value of 0 for factor 9.

In the non-limiting vehicle purchasing decision example, for factor 10,or exterior vehicle color, the user entered “red, green, blue” in firstdata column 234 and “white, black, yellow” in second data column 236. Insuch an example, in the event a vehicle includes an exterior color ofred, green or blue, engine 118 may calculate a numeric value of 10 forfactor 10. In the event the vehicle includes an exterior color of white,black or yellow, engine 118 may calculate a numeric value of 0 forfactor 10. Further, in the event that the vehicle color is neither, red,green, blue, white, black nor yellow, engine 118 may alert the user ofthe vehicle's color.

In the event the user wishes to exclude a factor from the decisionanalysis, the user may switch ON/OFF button 240 in the factors page 200to “OFF” for the factor. As a result, engine 118 will not consider thatfactor in the decision analysis.

In method step 308, once the numeric value of each factor of eachdecision option 258 is calculated, system 100, and more specificallyengine 118 may calculate a weighted score for each factor using theimportance assigned to each factor by the user. Scores of each factormay be weighted in any manner, for example by applying a higher weightto scores for factors having a greater importance to the user. Further,the weighted scores can be based on any scaling method and, should notbe limited to the numbers or scales shown herein. In the non-limitingvehicle purchasing decision example, the user ranks factor 1 and 2, orMPG and cost, as the most important factors in the decision analysis andtherefore assigns an importance ranking of 100 to factors 1 and 2.Further, in the exemplary embodiment, the user assigned an importancevalue of 50 to factor 3, or miles per tank. Moreover, the user assignedan importance value of 25 to factors 4-10. As a result, in this example,the user desires an affordable automobile that achieves substantiallygood gas mileage and may be driven a relatively far distance on a singletank of gas. However, other factors, such as interior noise level,acceleration, turning radius and ABS braking are other factors that,while not as important as cost, MPG and miles per tank, they mayinfluence the decision of the user in one way or another.

For example, factor 1, or MPG factor of decision option A, gets 42 MPG.The user indicated that the most desirable MPG for an automobile is 35MPG. The MPG factor for automobile A is greater than the most desiredvalue and as a result, MPG factor receives a score of 10. Further, theuser assigned an importance of 100 for the MPG factor. As a result, theMPG score is multiplied by the importance level. As such, the exemplaryweighted score for the MPG factor for automobile A is 10.times.100=1000,as shown in FIG. 7. In the event the MPG was 22.5, as described above,the MPG score would be 5 and the weighted score would be5.times.100=500. Moreover, the interior noise level factor received 7out of 10 stars, therefore, receiving a score of 7. The user assigned animportance level of 25 for this factor. As a result, the weighted scoreof the interior noise level is 7.times.25=175. Further, the exteriorcolor of automobile A is red, resulting in a score of 10. The userassigned an importance level of 25 to this factor. As such, the weightedscore of the exterior color factor is 10.times.25=250. Similarcalculations are performed for all desired decision options.

Once the weighted score for each factor is calculated by engine 118, thescore for each factor and total score for each decision option 258 maybe displayed in results page 270, as shown in FIG. 7. In the exemplaryembodiment, each automobile, or decision option 258, is displayed withthe weighted score of each factor. Moreover, the total score of eachautomobile is calculated by engine 118 and displayed for each decisionoption 258. In the non-limiting vehicle purchasing decision example,decision option A, or automobile A, received a total score of 2553,automobile B received a total score of 1960, automobile C received atotal score of 2359 and automobile D received a total score of 2118.

In method step 310, as shown in FIG. 10, engine 118 ranks each decisionoption based on the total score. In method step 312, engine 118 displaysa graphical representation of the total scores of each decision option,or automobile, may be displayed in graph results page 280, as shown inFIG. 8. In one embodiment, engine 118 may display the ranks of eachdecision option based on the total score. Once the total scores of eachdecision option or automobile are calculated by engine 118, the user maydisplay summary pop-up window 294 on any page by clicking summary button220, as shown in FIG. 9. Alternatively, summary pop-up window maydisplay any decision analysis information. As such, the user mayinterpret from FIG. 8 that automobiles options A and J include amajority of the user's desired factors. As a result, system 100 enablesthe user to choose a decision option based on at least one factor.

Referring again to FIG. 1 as well as FIG. 14, system 100 may include atleast one server 112. Server 112 may include first engine 118, secondengine 120 and third engine 122. In at least one embodiment, firstengine 118 may be a decision engine, second engine 120 may be aconnector engine and third engine 122 may be a response engine.

Connector engine 120 may be an interfacing application for facilitatingdata sharing between decision engine 118 and response engine 122.Response engine 122 may process the shared data and respond with anytype of response. For example, response engine 122 may process thepresence or absence of factors, the presence or absence of groups offactors, the presence or absence of decision options, the presence orabsence of raw data, the presence or absence of thresholds for factorimportance ratings, the presence or absence of thresholds for factorweights, the presence or absence of thresholds for group factor weights,the presence or absence of thresholds for decision option ranks, thepresence or absence of thresholds for decision option scores, the boundsof factor definitions, the bounds of normalized factor definitions, thebounds of weighted factor definitions and the like. Response engine 122may respond with advertisements, alerts, information, grades, URLs,merchants, coupons, opportunities, video, audio, queries, analyses andany other response known to one having ordinary skill in the art. In atleast one exemplary embodiment, response engine 122 may be amarketing/advertising engine for responding with types of vendor-relateddata.

Decision engine 118 may be any embodiment described above or any otherdecision engine known to one having ordinary skill in the art. Decisionengine 118 may be capable of serving a plurality of first users 104operating client computing devices on a user-accessible portion ofnetwork 110, such as the Internet. In web-based embodiments, users 104may be provided access to decision engine 118 for comparing decisionoptions, for example and referred to hereinafter, but not limited to,products over the Internet through the use of suitable web-browsersoperating on the client computing devices. Decision engine 118 mayreceive a plurality of user inputs and process decision-related data inaccordance with the plurality of user inputs. Decision engine 118 maytransmit a plurality of product results (e.g., in the form of decisionoptions, relevant products, ranked products, etc.) via network 110 tothe client computing devices for consideration by users 104. In at leastone embodiment, the one or more product results may be scored, in totaland/or by factor, and listed for display. The product results may belisted by rank, for example, with the highest scoring product appearingfirst in the list. Also, in at least one embodiment, the comparison ofproducts may be based on decision options selected by user 104 from apredetermined set of products made available for user-selection or entryby decision engine 118.

In at least one embodiment, decision engine 118 may provide one or moreproduct modules for comparing types and/or classes of products. Asnon-limiting examples, products modules may be provided for automobiles,real estate, schools, employment or any other type of product, serviceor action which may utilize a decision. Moreover, in a non-limitingmanner, automobile product modules may be provided for economy vehicles,sport vehicles, sport utility vehicles, luxury vehicles and familyvehicles.

Decision engine 118 may provide a plurality of relevant factors forselection by users 104 within each product module. As described above,all or less than all of the plurality of factors may be furtherdefinable by at least one of a range of numeric values (broadly read soas to also include a single numeric value), Boolean designations (i.e.yes/no answers and true/false answers), multiple-choice values/answersand any other data for defining factors known to one having ordinaryskill in the art. Also, numeric ranges defining factors may include bestand worst values with intermediate values defined between the two usingany method known to one having ordinary skill in the art. Booleandesignations and multiple-choice values may also be used to define bestand worst values for the factors. Subjective factors may havepre-supplied definitions, such as those provided by third partyentities. For instance, opinion definitions may be rated on a scale of 1to 10 stars or any other scale known to one having ordinary skill in theart.

As described above, all or less than all of the plurality, of factorsmay be assigned importance levels/ratings by user 104. The importanceratings may be used by decision engine 118 to weigh the factors. In atleast one embodiment and as described above, user 104 may select, defineand assign importance ratings to factors through one or more navigationscreens/windows presented by decision engine 118 to user 104 via thegraphical user interface and display of a client computing device.

In processing the decision-related data, decision engine 118 may definefactors in accordance with the user inputs. For instance, a factor maybe definable by numeric range. User 104 can input the numeric values (ornumeric value) setting the numeric range through data entry provided bydecision engine 118. Also, a factor may be definable by one or moreBoolean designations/conditions. User 104 may select one of the twoanswers for each Boolean condition through data entry provided bydecision engine 118. Moreover, a factor may be definable by one or moreanswers out of multiple available values. User 104 may select one ormore answers for each set of multiple-choice values through data entryprovided by decision engine 118. In at least one embodiment, subjectivefactors may not be definable by user 104, but may be defined by decisionengine 118 (through, for example, third party ratings) and user 104 mayselect which factors may be considered. All or less than all of thefactors selected by user 104 for consideration may be normalized on astandardized numerical scale (e.g., 1 to 10). Factors may then beweighed in accordance with user inputs for weighing such factors, whichmay be predominantly or wholly based on the importance ratings assignedto the factors by user 104.

As such, the decision-related data may include the factors, importanceratings for the factors and weights for the factors. Factor weights maybe expressed as percentages, as one non-limiting example. Thedecision-related data may also include definitions for factors, whether,for example, by numeric scale, Boolean conditions, multiple-choiceanswers or subjective assessments. The decision-related data may alsoinclude product results (e.g., decision options), ranks for productresults and scores (whether weighted or unweighted) for factors andproduct results. The decision-related data may also include data aboutuser 104 and/or the client computing device of user 104. Thedecision-related data may additionally include metadata associated withany of the above or any other needed data, as will be readily recognizedby one having ordinary skill in the art. All or part of thedecision-related data may be stored on at least one database 114.

Still referring to FIGS. 1 and 14, in at least one embodiment, marketingengine 122 may be interfaced with decision engine 118. Marketing engine122 and decision engine 118 may be interfaced via interfacingapplication 120. Interfacing application 120 may allow fordecision-related data processed by decision engine 118 to be shared withmarketing engine 122. Marketing engine 122 may also process thedecision-related data and process or provide any desired sales,advertisement, coupon or related data. Decision engine 118 and marketingengine 122 may process the decision-related data in parallel or nearlyin parallel. Marketing engine 122 may transmit vendor-related data, orany other desired sales, advertisement, coupon or related data to theclient computing devices of users 104 in accordance with thedecision-related data.

The vendor-related data may include vendor advertisements, vendorlocations, vendor ratings, vendor website hypertext links, vendorcoupons, any combination thereof and like data known to one havingordinary skill in the art. The vendor-related data and anydecision-related data may be transmitted together so as to be displayedto user 104 on a single web page or single set of web pages (e.g.,related navigation screens). For example, a web page displayed to user104 may show scored product results and may show vendor advertisementstherewith. All or part of the vendor-related data can be stored on atleast one database 114.

Vendor-related data may also include vendor marketing profiles thatmarketing engine 122 may process and match to the decision-related datathat is also processed by marketing engine 122. The vendor marketingprofiles may establish criteria for transmitting the othervendor-related data, such as vendor advertisements. The vendor marketingprofiles may establish criteria for sending other vendor-related data(e.g., vendor advertisements) based on decision factors, importanceratings for the decision factors, importance ratings for the factors (orfor groups of the factors), weights for factors (or for groups of thefactors), ranks for the factors, definitions for the factors (and theunderlying data defining the factors), product results (e.g., decisionoptions), ranks for the products results, scores for the factor orproduct results, any combination thereof and the like.

Interfacing application 120 may tie marketing engine 122 to decisionengine 118. Interfacing application 120 may be on at least one server112. A primary purpose of interfacing application 120 may be tofacilitate data between decision-engine 118 and marketing engine 122. Inparticular, interfacing application 120 may facilitate sharing of all orpart of the decision-related data with marketing engine 122. As such,interfacing application 120 may provide the needed decision-related datato marketing engine 122.

Accordingly, an interfacing computer program product stored on acomputer storage medium may include a computer program mechanismembedded in the computer storage medium for causing a computer tointerface decision engine 118 and marketing engine 122. The computercode mechanism may include a computer code device configured tointerface with decision engine 118. The computer code mechanism may alsoinclude another computer code device configured to interface withmarketing engine 122. The computer code mechanism may further includeyet another computer code device configured to facilitate data sharingbetween decision engine 118 and marketing engine 122.

FIGS. 11 and 12 illustrate marketing profile data entry page 1100 andmarketing profile data entry page 1200, respectively, that may be usedwith system 100, particularly, with marketing engine 122. In web-basedembodiments, data entry pages 1100, 1200 may be web page forms providedby marketing engine 122 to the client computing devices of second users106 via network 110. Second users 106 may be any entity or partyinvolved in marketing or advertising, such as, but not limited to,advertising agencies, advertisers and specific product entities.

Marketing engine 122 may effectuate electronic advertising by providingusers 106 with marketing profiles to establish criteria for conductingelectronic advertising campaigns. Accordingly, marketing engine 122 maypresent one or more user-fillable marketing profile forms to users 106for data entry of inputs defining criteria for an electronic advertisingcampaign. The marketing profiles and associated forms may be provided innumerous configurations and designs, as will be readily recognized byone having ordinary skill in the art. As such, marketing profiles maymake use of numerous data entry fields and GUI widgets in providingtillable forms to users 106.

Referring particularly to FIG. 11, web page form 1100 for establishingcriteria for conducting an electronic advertising campaign isillustrated in accordance with at least one exemplary embodiment.Marketing profile form 1100 may include a plurality of data entry fieldsfor accepting user inputs corresponding to required or optional campaigninformation and criteria. Form 1100 may also provide directions thatguide users 106 to fill out form 1100 by the way of data inputs forestablishing or modifying an electronic advertising campaign.

Form 1100 may be directed to a running auction-style of electronicadvertising campaigns, which may operate, at least in part, based on abid-per-click system. Accordingly, the electronic advertising campaignmay be priced based on a pay-per-click system or may be based and pricedon any other system known to one having ordinary skill in the art. Therunning auction-style of electronic advertising campaigns and thepay-per-click pricing systems are well-known to one having ordinaryskill in the art and further discussion directed thereto will be limitedor omitted herein.

Form 1100 may include numerous sections of which the following areexemplary of. Form 1100 may include a title/heading section 1102. Asshown in an illustrative and non-limiting manner, form 1100 may beentitled “Advertising Campaign Dates, Clicks and Dollars.” Section 1104of form 1100 may provide for data entry regarding account identifiers,such as, but not limited to, account name and account number. In atleast one embodiment, account identifiers may be automatically providedby marketing engine 122 to user 106 who may be logged into their useraccount. Alternatively, user 106 may input data for identifying anaccount.

Form 1100 may include present date and campaign start date section 1106.The present date may be automatically provided to user 106 by marketingengine 122 and the campaign start date may be selected by user 106, asone non-limiting example. Form 1100 may also include section 1108establishing campaign termination criteria. User 106 may input dataestablishing such termination criteria. As shown in an illustrative andnon-limiting manner, termination criteria may be conditioned in thealternative with the first occurrence of any of the termination criteriaresulting in the termination of the electronic advertising campaign. Forinstance, the alternative termination criteria may be a terminationdate, an amount of click-through or a campaign budget ceiling.

Form 1100 may further include bid-per-click section 1110. As shown in anillustrative and non-limiting manner, bid-per-click section 1110 may beused to establish a present bid-per-click and a maximum bid-per-click.User 106 may input such data and later be subjected to a running auctionconducted by marketing engine 122 based on bid-per-click data.

Moreover, form 1100 may include product module section 1112. In at leastone embodiment, decision engine 118 may provide and be applied todifferent products separately through a plurality of product modulesfocused on comparing certain types and/or classes of products. User 106may only advertise in a product module(s) that user 106 deems mostrelevant to the goods and/or services user 106 is marketing.Accordingly, user 106 may select which product module(s) to apply theelectronic advertising campaign to by inputting such into product modulesection 1112. In at least one embodiment, user 106 may select more thanone product module to effectuate the electronic advertising campaignwithin.

Furthermore, form 1100 may include keyword section 1114. In at least oneembodiment, decision engine 118 may provide for keyword-based searchingand comparisons alone or in combination with factor-based comparisons.For example, keyword-based searching may be provided by decision-engine118 in order to allow first user 104 to identify one or more relevantproduct modules in which to perform a factor-based comparison. Seconduser 106 may select one or more keywords to apply an electronicadvertising campaign to.

Referring particularly to FIG. 12, web page form 1200 for establishingcriteria for conducting an electronic advertising campaign isillustrated in accordance with at least one exemplary embodiment. Form1200 may function as a continuation or counterpart of form 1200 inestablishing campaign criteria. In particular, form 1200 may be directedto establishing factor-based and results-based criteria for theelectronic advertising campaign. Form 1200 may include a plurality ofdata entry fields for accepting user inputs corresponding to required oroptional campaign information and criteria. Form 1200 may also providedirections that guide users 106 to fill out form 1200 by the way of datainputs for establishing or modifying an electronic advertising campaign.

Factor-based and result-based form 1200 may include numerous sections ofwhich the following are exemplary of. For example, form 1200 may includesections 1202, 1204, 1206, 1208, 1210, 1212 directed to establishingcriteria based on factors of interest, importance ratings for thefactors, weights (e.g., by percentage) for the factors, weights for afirst group of factors, weights for a second group of factors, rank ofvendor's product in results list, respectively, and any other suitablesections for establishing criteria, as will be readily recognized to onehaving ordinary skill in the art.

Factors of interest section 1202 may include one or more factors ofinterest 1214 subject to criteria being established in relation thereto.In at least one embodiment, factors of interest 1214 may be provided insection 1202 by marketing engine 122. Users 106 may establish criteriain relation to factors of interest 1214 automatically provided bymarketing engine 122.

Importance rating section 1204 may be provided to user 106 by marketingengine 122 for inputting a minimum level of importance for any factorthat would trigger the electronic advertising campaign. For example, byplacing an importance rating value in an appropriate data entry field1216 for a factor, user 106 may set the importance rating floor for thatfactor.

Individual factor weight section 1206 may be provided to user 106 bymarketing engine 122 for inputting a percentage of the overall decisionfor any factor that would trigger the electronic advertising campaign.For example, by placing a weight value in an appropriate data entryfield 1218 for a factor, user 106 may set a percentage floor for thatfactor.

Either or both of first group weight section 1208 and second groupweight section 1210 may be provided to user 106 by marketing engine 122for inputting a percentage of the overall decision for any group offactors that would trigger the electronic advertising campaign. Forexample, by selecting more than one factor by placing indicia in theappropriate data entry fields 1224, 1226 and setting a percentage floorby placing a weight value in the appropriate date entry field 1220,1222, user 106 may set a cumulative percentage floor for the group ofselected factors.

Product rank section 1212 may be provided to user 106 by marketingengine 122 for inputting a position for their product within a resultslist that would trigger the electronic advertising campaign. Forexample, by placing a rank value in data entry field 1228, user 106 mayset a rank floor for triggering the electronic advertising campaign.

FIG. 13 is a flow chart of exemplary method 1300 of providingvendor-related data to a client computing device in response todecision-related. Method 1300 may be performed by marketing engine 122in accordance with the vendor marketing profiles. In method step 1302,marketing engine 122 may receive decision-related data processed bydecision engine 118. The decision-related data available from decisionengine 118 may include, but is not limited to, factors (includingrequired and optional factors), factor importance ratings, factorweights, raw data (e.g., numerical, descriptive, perspective, subjectiveand objective), factor definitions, normalized factor definitions,weighted factor definitions, factor scores, product results, productranks, product scores and the like.

In method step 1304, marketing engine 122 may process thedecision-related data in accordance with the vendor marketing profiles.For example, marketing engine 122 may match decision-related data to thevendor marketing profiles in determining what, if any, vendor-relateddata should be sent to a client computing device of user 104.

In accordance with the vendor marketing profiles for each productmodule, marketing engine 122 may process (and match) the presence orabsence of factors, the presence or absence of groups of factors, thepresence or absence of product results, the presence or absence of rawdata, the presence or absence of thresholds for factor importanceratings, the presence or absence of thresholds for factor weights, thepresence or absence of thresholds for group factor weights, the presenceor absence of thresholds for product ranks, the presence or absence ofthresholds for product scores, the bounds of factor definitions, thebounds of normalized factor definitions, the bounds of weighted factordefinitions and the like.

Marketing engine 122 may also process the vendor marketing profile datato determine the status (e.g., active or inactive) of electronicadvertising campaigns, bid-per-click data (e.g., present or maximum) andthe like. Similarly, marketing engine 122 can process or providesales-related data, facilitate sales and provide any other data relevantto sales or which may be desired to facilitate sales, such asadvertisements or coupons. Marketing engine 122 may also apply rulesgoverning the running auction in determining which vendor-related datais to be transmitted and in what order of priority it will be displayedon a client computing device of user 104.

In method step 1306, marketing engine 122 may transmit thevendor-related data, as determined, to a client computing device of user104 via network 110. The vendor-related data may include any of vendoradvertisements, vendor locations, vendor ratings, vendor websites links,vendor coupons, text, audio, video, images, database query answers, anycombination thereof and like data known to one having ordinary skill inthe art. In some embodiments, the vendor-related data may be primarilyin the form of electronic advertisements providing hypertext links tothe websites of users 106 or any other supply or information source, butis not so limited. Moreover, electronic advertisements and any othersuitable vendor-related data may appear on any GUI screen (e.g., resultspages, navigation windows, etc.). Furthermore, the electronicadvertisements may be provided anywhere (above, below, to the side of,etc.) in relation to a list of product results or any otherdecision-related data on a web page displayed on a client computingdevice of user 104.

In still another exemplary embodiment, and referring to FIG. 15,examples of decision factors and scoring may be shown. In this example,a number of factors may be used in a decision making process. Here,through interface 1500, a number of exemplary factors 1502 may be shown.Additionally, a bar graph 1504 that may correlate to a percentage ofoverall decision, or weight, 1506 may be displayed. Finally, a totalvalue 1508 may be represented at a bottom portion of the interface 1500.The factors 1502 and their manner of being displayed in interface 1500may act as both numerical and graphical representations of a user'simportance settings when he or she is engaged in a decision makingprocess.

In the example shown with FIG. 15, a fingerprint, or set of factors,such as decision-related factors, that pertain to a desired user, may bedetermined, realized or utilized. The fingerprint may be used to compareoptions from within a predetermined or specific set of decisionpossibilities or outcomes. For example, because each user of a decisionmaking engine, such as those described in previous examples, may beunique insofar as they may each generate a different mix of factorweights, these uniqueness of each user may result in different rankingsof the available options. Thus, through a comparison of the differentfactors 1502 and weights 1506 of each user's decision making, variousdynamics of the decision may be further understood and explored. Thus,any factors and weighting chosen by a user may be stored and compared inany desired manner. For example, the factors and weighting of any numberof different users may be compared. Similarly, the factors and weightingof any number of users may be compared with the factors and weighting ofone or more experts in a desired field. Additionally, a fingerprint ofany user may be saved automatically or may be saved as desired at anytime. Any fingerprint data may then be stored in a database or multipledatabases for any desired form of comparison.

As shown in the example with respect to FIG. 15, a user may base apurchasing decision on five factors 1502, for example price, gasmileage, reliability, navigational system and exterior appearance. Theuser may then apply a weight 1506 to each factor and the weight may beshown both in numerical form 1506 as well as graphical form 1504.Additionally, although a bar graph is shown in exemplary FIG. 15, anyform of graph or any other representation of weight 1506 may beutilized. In the present example, a user has given a weight of 26.1% tothe factor of price, 21.7% to the factor of gas mileage and equalweights of 17.4% to the factors of reliability, navigational system andexterior appearance. The total 1508 is thus shown as 100%. Thisinformation, when viewed together, may be saved or presented as afingerprint of that particular user for the exemplary purchasingdecision shown in FIG. 15.

Fingerprint data, or saved user data regarding a purchasing decision,may then be used in any of a variety of manners. For example, differentusers may view fingerprint data, perhaps anonymously, of other users whomay have engaged in a similar purchasing decision. Also, differentexemplary fingerprints may be shown to provide a user with the variousoptions and alternatives that may be displayed as the weighted factorsare varied. Further, a database of fingerprint data from any number ofusers of a decision making engine may be collected and used in anyfurther manner desired. In one exemplary, various demographics may beviewed and polled for their desired weighted factors in a purchasingdecision.

One exemplary manner of viewing demographic data, as shown in exemplaryFIG. 16, could be querying a database to determine a list of averageweighted factors for purchasing an automobile for any desiredcharacteristics, such as asking what predetermined sex of users of thedecision making engine, who live in a predetermined geographical area,have a predetermined income and have any other predeterminedcharacteristics. As shown on interface 1600, the factors 1602 of suchusers may be shown to apply weights 1606 (and as shown in graphs 1604)may represent the combined or averaged fingerprint data for any numberof users fulfilling any desired criteria.

In another exemplary embodiment, and as shown in exemplary FIG. 1700, auser may be presented with or a decision making engine may otherwiseemploy a default set of fingerprint data or default set of weightedcharacteristics. In these exemplary embodiments, any default data may bebased on a predetermined or historical interpretation of data.Additionally, any number of factors may be utilized in the default data,for example the use of ten factors and their respective default weights.Further, the amount of default fingerprint data may be changed or variedin any desired manner. For example, if a user is presented with a listof default data, which could correspond to the data shown in interface1700, the user may be able, to add or remove any number of factors so asto refine the list of default fingerprint data to a greater or smallernumber of factors. This can assist a user who may or may not know apredetermined amount to weight a factor in a decision or purchase theymay be planning to make. In the example shown in FIG. 17, the user maydecide that a factor such as exterior appearance in the list of defaultfactors 1702 is not important to that user. This factor may then beremoved, along with its corresponding weight 1706. Thus, a user may bepresented with a new or varied list of choices as based on the alteredfactors. Similarly, any new factor not shown on the list of factors 1702may be added as desired.

In another exemplary embodiment, and as shown with respect to FIGS. 18and 19, after fingerprint or any other data is saved for a user, theuser may input any desired product data or datasets into a decisionengine. The decision engine may then provide the user with a score basedupon the user's desired factors 1802 and weights 1806 (or importancesettings). In the examples of FIGS. 18 and 19, an automobile purchasedecision may be contemplated by the user. The factors 1802 and weights1806 may then be inputted per the user's fingerprint data andcorresponding graphs 1804 may be shown. A decision engine may thenprocess this data and, based upon any available automobile, provide theuser with scores and a best option.

As shown in exemplary interface 1900 of FIG. 19, a series of scores anda best choice may be shown. Based upon the information shown amongst thefingerprint data of exemplary FIG. 18, two or more choices, in this caseautomobiles, may be returned to the user with their respective scores.Similar to the comparison methodology discussed previously, a firstautomobile may show scores 1902 based upon an equal weighting of allavailable factors. The first automobile may then show scores 1904 basedupon the weighted factors from the user's fingerprint. Similarly,weighted and unweighted scores 1906 and 1908, respectively, may be shownfor a second automobile choice. These scores may then be totaled in abottom portion 1910 of interface 1900. At a top portion of interface1900, the two weighted scores using the user's fingerprint of data maythen be shown 1912. The automobile, or any other product or decisionchoice otherwise, with the highest score with respect to the fingerprintdata weighted values of the user may then be highlighted in any desiredmanner, for example by displaying the word “WINNER” proximate to thehighest score.

In a further example of the use of fingerprint data, as shown inexemplary FIGS. 20 and 21, different factors 2002 and weights 2006 (asfurther shown with graph 2004) may be saved or otherwise known as thefingerprint data of a user. The decision making engine may then processthe data and provide the user with the output scores 2102 through 2108,the totals 2110 and the display scores 2112 at a top portion ofinterface 2100 so as to clearly show the product choice with the highestscore.

In still another exemplary embodiment, any of the fingerprint data thatmay be stored for a user in the examples related to FIGS. 16-21 may beutilized on a mobile device, such as a mobile computing device. In thisexample, a user may be at a location, such as a store, that sellsproducts that a user may be interested in purchasing. There may,however, be a variety of products from which the user may desire tochoose. Thus, a user may input data regarding the physical product inany desired manner into their mobile device. Exemplary manners ofinputting data may include typing a name, typing a serial number,photographing a product, photographing a serial number, scanning abarcode, photographing a barcode or any other manner of inputting datadesired. The user's mobile device may then be connected with a decisionmaking engine similar to any of the exemplary embodiments describedherein. Additionally, the mobile device may be connected to the decisionmaking engine in any desired fashion. Using fingerprint or any otheruser-related input data, the decision making engine may process the datainputted by the user into the mobile device. The processing of the datamay allow for the user to receive a number of scores, for exampleweighted scores as described herein, on their mobile device. The usermay then be able to purchase an item based upon the scores provided tothem.

FIG. 22 is a block diagram of a communications network 2200 forimplementing an embodiment of the present disclosure. The communicationsnetwork 2200 includes a collection of communication linksinterconnecting a plurality of nodes, such as communication units 2215a-b, access points 2220 a-b, intermediate nodes 2230 a-n, connectorengine 2205, marketing database/engine 2235, and decision engine 2210.These interconnected nodes communicate with each other by exchangingdata packets according to a pre-defined set of network protocols, suchas the Transmission Control Protocol/Internet Protocol (TCP/IP) and theSession Initiation Protocol (SIP). A network protocol as used herein isa formal set of rules that define how data is exchanged between nodes ina communications network.

As described in more detail herein, the connector engine 2205 may beemployed to improve, customize, or otherwise modify a marketing campaignby connecting the decision engine 2210 to the marketing database/engine2235 to provide a user of communication devices 2215 a-b with dataassociated with a marketing campaign (e.g., vendor-related data). Beforedescribing the connector engine 2205 in more detail, a description ofthe communications network 2200 is provided. It should be understoodthat the connector engine 2205 may be employed in other networktopologies or applications, such as single processor machines.

Communication units 2215 a-b may be conventional communication units,such as laptop computers, desktop computers, wireless transmit/receiveunits (WTRUs) (e.g., wireless telephones, personal digital assistants(PDAs), and smart phones), IP telephones, and the like, that convertinformation (e.g., data) into signals that are transmitted to the accesspoints 2220 a-b via wireless links.

The access point 2220 a contains logic that enable the communicationunits 2215 a-b to transmit the information (e.g., data) to the decisionengine 2210 via the network 2250. For example, the access point 2220 amay include circuitry configured to receive signals (e.g., radiofrequency (RF) signals), from the communication units 2215 a-b, thatcarry the information via wireless links. Once the signals are received,the access point 2220 a converts the signals into data packets accordingto the pre-defined set of network protocols. The access point 2220 athen transmits the data packets to the network 2250 via gateway 2222 a.The access point 2220 b transmit data/information is a similar manner asthe access point 2220 b. In addition, the access points 2220 a-b convertdata packets received from the network 2250 into signals and transmitthe signals to the communication units 2215 a-b and the decision engine2210 via wireless links.

Examples of access points 2220 a-b that may be used with the presentinvention include certain Institute of Electrical and ElectronicEngineers (IEEE) 802.11 compliant access points as well as certaincellular telephone wireless systems that support the transmission oftraffic (e.g., data traffic). Other forms of access points now known orhereafter developed are contemplated to be operable with embodiments ofthe present disclosure.

The network 2250 receives the data packets from the gateways 2222 a-band transmits the data packets to a destination via the intermediatenodes 2230 a-n. The destination can be defined by a destination address.In an example, the destination address is located in a header of thedata packets. The intermediate nodes 2230 a-n are typically conventionalintermediate nodes, such as routers, that are configured to implement acommunications protocol such as an Internet Protocol (IP) or Voice OverInternet Protocol (VoIP) network 2250.

It should be noted that embodiments of the present disclosure may beadopted to work with fixed as well as mobile devices that are able tocommunicate with a communication network. Examples of fixed devicesinclude telephone units, personal computers, and the like that are wiredto a network.

As illustrated, the connector engine 2205 may be located at anintermediate point of the network 2250 between the communication units2215 a-b and decision engine 2210. Optionally, the connector engine 2205may be logically or physically coupled directly to the communicationunits 2215 a-b and/or decision engine 2210.

The connector engine 2205 employs an interface (as illustrated in FIG.23) which enables vendors (associated with marketing engines 2235) toprovide marketing data (e.g., vendor-related data such asadvertisements, coupons, and discounts) to users of the communicationunits 2215 a-b in communication with the decision engine 2210. In someexamples and as described above in reference to FIGS. 6-10, the users ofthe communication units 2215 a-b access the decision engine 2210 inorder to compare decision options, for example, products/services overthe Internet. For instance, the user may wish to purchase a camera(i.e., a product) and may utilize the decision engine 2210 to assistwith identifying cameras and ultimately deciding which specific type ofcamera to purchase, such as a Canon® camera or a Nikon® camera (i.e.,decision options). In such a scenario, vendors, whose products are notassociated with the decision engine 2210, may wish to provide marketingdata to the users in response to at least one of the following: the userinteraction with the decision engine 2210 or results of the decisionengine 2210.

For instance, the user may utilize the decision engine 2210 to make adecision to purchase a camera by utilizing data entry interface page 200(as illustrated in FIG. 3). In this example, the decision engineprovides the user with factors on the page 200 that users generallyconsider when purchasing a camera (e.g., price, lens type, and zoomcapabilities). Based on a user's input with respect to each of thefactors 232 (see FIG. 3) (e.g., input to the data fields 234, 236 (seeFIG. 3) and application of importance values 245 (see FIG. 3) to eachfactor), the decision engine 2210 provides a selection of cameras (e.g.,decision options) to the user to consider in, making a decision topurchase a camera (see FIG. 8-9).

In some scenarios, the decision engine 2210 may not present a cameraassociated with a vendor as a decision option (i.e., a result of thedecision engine). In this case, the vendor may wish to provide the userwith marketing data associated with a camera sold by the vendor. Inaddition, the vendor may wish provide the user with marketing dataassociated with a camera that best fits the user's input with respect tothe factors 232 (see FIG. 3) (i.e., the user interaction with thedecision engine 2210).

In an embodiment of the present disclosure, the connector engine 2205monitors the network 2250 for communications between a user of one ofthe communication devices 2215 a-b and decision engine 2210 over network2250. The connector engine 2205 may monitor SIP messages (or other typeof messages) that indicate whether the user is attempting initiate acommunications session with a server known to employ a decision engine2210. For instance, the connector engine 2205 may compare destinationaddresses associated with the messages to a lookup table that includesservers known to be hosting decision engines. If the destination addressmatches an address associated with such a server, the connector engine2205 determines that the user is initiating a communication sessionbetween the user and a decision engine.

Once the connector engine 2205 determines that a communication sessionis occurring between a user and a decision engine, the connector enginemonitors and retrieves communication data between the user'scommunication device 2215 a-b and the decision engine 2210. As statedabove, a user may be utilizing the decision engine 2210 because the userwants the decision engine to provide decision options associated withthe user's desire to purchase a good/service (e.g., a camera). Thedecision engine 2210, as stated above, may provide a user interface (asillustrated in FIGS. 3-9) for the user by which the user may interactwith in order to facilitate the decision engine 2210 to provide decisionoptions to the user (e.g., a set of specific cameras for which the userto consider to purchase). In an example and as stated herein, thedecision engine 2210 may provide a user with a plurality of relevantfactors associated with a product/service of interest. In this example,the user may be interested in cameras. Thus, the decision engine mayprovide a list of factors 232 (see FIG. 3) (e.g., lens, price, weight,digital information, and zoom) to the user as described above inreference to FIGS. 3-4. At the end of the user's interaction with thedecision engine 2210, the decision engine 2210 outputs at least onedecision option (e.g., a suggested camera to purchase) to the user.

The connector engine 2205 receives data associated with the user'sinteraction with the decision engine 2210. For example, the connectorengine 2210 receives the user inputs with respect to each of the factors232 (see FIG. 3) In addition, the connector engine 2210 receives theimportance values provided by the user with respect to each factor 232(see FIG. 3) and the results output by the decision engine 2210 (e.g.,decision options (see FIGS. 6-9).

In response to receiving the data associated with the user's interactionwith the decision engine 2210, the connector engine 2210 retrievesmarketing data (e.g., vendor-related data) from the marketingdatabase/engine 2235. In an example, the connector engine 2210 mayretrieve the marketing data by submitting a request to the marketingdatabase/engine 2235 for marketing data that corresponds to the user'sinteraction with the decision engine 2210. In response to the request,the marketing database/engine 2235 returns relevant marketing data tothe connector engine 2210. The marketing database/engine 2235 may be acentral server including a plurality of marketing databases/enginescorresponding to a plurality of vendors. Alternatively, the connectorengine 2210 may be coupled to a plurality of marketing database/engines,each database/engine corresponding to a single vendor. In this example,the vendors may be a manufacturer of cameras such as Nikon, Canon, andKodak®. The vendors may also be retailers that sell said cameras such asBest Buy, Wal-Mart, and Sears. Alternatively, the vendors may be anyentity that would like to provide marketing data in response to theuser's desire to purchase a camera (e.g., airlines and travel bookingentities).

In order to determine which vendors offer products or services that areresponsive to a user's desires, and, thus, the marketingdatabases/engines from which to retrieve marketing data (e.g.,vendor-related data), the connector engine 2205 matches at least onefactor of the decision-related data with vendor factors associated withthe marketing data. The connector engine 2205 then obtains the marketingdata associated with at least a subset of the matched vendor factors. Inresponse to selecting the marketing data, the connector 2205 processesthe received data associated with the user's interaction with thedecision engine 2210 (e.g., modified decision-related data (factors))and modifies the vendor factors associated with the selected marketingdata. The connector engine 2205 then selects a subset of the marketingdata (e.g., vendor-related data) based on the modified vendor factors.

FIG. 23 is a block diagram of an example embodiment of a connectorengine 2305 that includes an interface 2301, presenting module 2303,selection module 2305, ranking module 2311, and retrieving module 2307.

The interface 2301, which is provided on one or more processors, isconfigured to receive data associated with a user's interaction viacommunication devices 2215 a-b with the decision engine 2210. Theinterface 2301 is also configured to pass the data to one of theaforementioned modules based on the type of data received. For example,the data received may be user inputs to the decision engine, modifieddecision-related data (e.g., factors as modified by the user inputs),and decision options (e.g., output of the decision engine 2210). Inresponse to receiving the data, the interface 2301 passes the receiveddata to one of the aforementioned modules as described in more detailherein.

The interface 2301 provides the received data (e.g., user inputs andmodified decision-related data) to the retrieving module 2307, which isalso provided on the one or more processors. The retrieving module 2307is configured to retrieve marketing data (e.g., vendor-related data)from a marketing database/engine based on one or more properties of themodified decision-related data (e.g., factors, values associated withthe factors based on user inputs, and importance values assigned to thefactors based on user inputs).

In an example, the retrieving module 2307 matches at least one factor232 (see FIGS. 3-4) to at least one vendor factor associated withvendor-related data (e.g., marketing data). A vendor associates vendorfactors with the vendor-related data such that an engine (e.g., theconnector engine 2305) can utilize the vendor factors as criteria forselecting the vendor-related data. The retrieving module 2307 thenobtains the marketing data (e.g., vendor-related data) associated withat least a subset of the matched vendor factors from a marketingdatabase. The matched vendor factors are the same as the factors used bythe decision engine as criteria for selecting a decision option (e.g., aproduct/service of interest to a user). However, in this instance thevendor factors, as stated above, are criteria for selecting thevendor-related data rather than criteria for selecting a decisionoption.

The retrieving module may select a marketing database that correspondsto a vendor based on whether or not goods and/or services provided bythe vendor correspond to the factors used by the decision engine ascriteria in selecting a decision option. The retrieving module 2307 thenpasses the retrieved vendor-related data, modified decision-relateddata, and the matched vendor factors to the selection module 2309.

The selection module 2309 is provided on the one or more processors. Theselection module 2309 is configured to process the modifieddecision-related data to modify the matched vendor factors. The matchedvendor factors are used as criteria by the selection module 2309 forselecting marketing data. For instance, the selection module 2309applies the user inputs to the matched vendor factors. The selectionmodule 2309 then applies the user inputs to the matched vendor factors.Once the selection module 2309 modifies the matched vendor factors, theselection module 2309 selects a subset of the marketing data (e.g.,vendor-related data) based on the matched vendor factors as modified bythe user inputs.

The selection module 2309 includes a ranking module 2311 that isconfigured to rank the selected marketing data by applying weightedfactor scores associated with the decision-related data to the matchedvendor factors. Based on the ranks, the selection module 2309 selects asubset of the marketing data. In addition, the selection module 2309 mayfurther refine the selection of the subset of the marketing data basedon an advertisement subscription of a vendor associated with themarketing data. The selection module 2309 may then provide the selectedsubset of marketing data to the presenting module 2303.

The presenting module 2309 is configured to provide the marketing dataalong with the output of the decision engine in a format (e.g., size,corresponding graphics, layout, etc) based on a type of communicationdevice the user is utilizing to communicate over the network 2250. Forinstance, a laptop/desktop computer may have more resources (e.g.,larger displays, memory, processing power) for displaying the decisionoptions and marketing data than, for example, a mobile phone. In anexample, the presenting module 2309 determines a type of communicationdevice the user is utilizing based on communication device informationreceived from the interface 2301.

Such communication device information may include information thatindicates the resources available to a communications device. Forexample, the operating system or browser that the communication deviceis employing to communicate over the network 2250 can provide anindication to the presenting module 2309 regarding the resourcesavailable to the user for displaying the marketing data along with theoutput of the decision engine (e.g., decision options). The presentingmodule 2303 may extract the information from communication messages(provided by the interface 2301) sent by the user over the network 2250.Alternatively, the presenting module may determine such informationbased on a format of the decision options output by the decision engine.Based on such communication device information, the presenting module2309 provides the vendor-related data and decision options in a formatthat is optimal for displaying such data on the user's communicationdevice.

The interface 2301 then receives the format information from thepresenting module 2303 and selected marketing data from the selectionmodule 2309 and i) provides the selected marketing data (in a formatdetermined by the presenting module 2303) to the user, ii) provides themarketing data to a marketing engine as a recommendation of marketingdata to provide to the user, and/or iii) triggers the marketing engineto provide the selected marketing data to the user. For example, theinterface 2301 may trigger the marketing engine to provide the selectedmarketing data.

FIGS. 24A-C are graphs illustrating decision logic utilized by aconnector engine. As stated above, the connector engine receivesmodified decision-related data from a decision engine (see FIG. 3). Inan example, the modified decision-related data may be factors 232 (seeFIG. 3) used as criteria in selecting decision options by the decisionengine. The factors 232 (see FIG. 3) may be modified (i.e., modifieddecision-related data) with importance values 246 (see FIG. 3)(e.g.,weights) corresponding to each of the factors 232 (see FIG. 3). Asillustrated, the factors 232 (see FIG. 3) are associated with a user'sdesire to purchase a product (e.g., a camera). The decision engine mayuse the factors 232 (see FIG. 3) to return at least one decision option(see FIG. 7-9) to a user. It should be known that many otherfactors/combination of factors may also be utilized by the decisionengine.

The connector engine then retrieves marketing data (e.g., vendor-relateddata) 2410 from a marketing database/engine. In the example illustratedin FIG. 24A, the marketing database/engine stores a table 2400 a ofvendor factors 2426 a and vendor-related data 2410 a. For each of thevendor factors 2426, the table 2400 a includes un-weighted vendor factorscores 2434 corresponding to each of the vendor Options A-D 2410 a(e.g., vendor-related data). The un-weighted factor scores 2434 may besubjective scores assigned by each vendor based on a marketing campaigninitiated by the vendor.

In order to retrieve the marketing data, the connector engine matchesthe factors 232 (see FIG. 3) with vendor factors 2426 associated withvendor-related data 2410 a. Based on the results of the matching, theconnector engine obtains vendor-related data 2410 b (see FIG. 24B). Forinstance, the factors 232 (see FIG. 3) may only match with a subset ofthe vendor factors 2426 a. The connector engine then retrieves, asillustrated in FIG. 24B, the matched vendor factors 2426 b, thevendor-related data 2410 b associated with at least a subset of thematched vendor factors 2426 b, and the corresponding un-weighted vendorfactor scores 2434 b.

FIG. 24B illustrates an example table 2400 b associated with NikonAdvertisements generated by the connector engine in response toretrieving the aforementioned data and receiving the modifieddecision-related data (see FIG. 3). In addition, the connector engineapplies the user inputs (i.e., importance values 246 (see FIG. 3)) tothe matched vendor factors 2426 b to generate weighted vendor factors2425. The weighted vendor factors 2425 have associated importance values2427. As illustrated, the vendor-related data 2410 b and associatedun-weighted vendor factor scores 2434 are associated with a singlevendor (e.g., Nikon). It should be noted that the connector engine maybe performing a similar analysis using a subset of the matched vendorfactors 2426 b with respect to at least one other vendor (e.g., Canon)of a plurality of vendors. In order to select vendor-related data bestsuited for the user among the plurality of vendors, the connector mustnormalize the un-weighted factor scores 2434 b. The connector mayutilize any known method of normalization.

Once the un-weighted vendor factor scores 2434 b are normalized, theconnector engine applies weights to each of the normalized un-weightedvendor factor scores. In the current example, the connector enginemultiplies the importance values 2427 associated with the weightedvendor factors 2425 by the normalized un-weighted factor scores toobtain weighted factor scores 2435 as illustrated in FIG. 24C. Inresponse to calculating the weighted scores 2435, now referring to FIG.24C, the connector engine calculates a total score 2430 corresponding toeach item of the vendor-related data 2410 b. For instance, the connectorengine calculates the total score 2430 for each item, in thisillustrated example, by summing the weighted factor scores for each itemof the vendor-related data 2410 b. Subsequently, the connector engineprovides a ranking 2415 to each item of the vendor-related data 2410based the calculated total scores 2430. In this example, the connectorengine assigns the item of the vendor-related data 2410 that has thehighest total score with the highest rank.

The connector engine may then select a subset of the vendor-related data2410 b based on respective rankings. In addition, referring to FIG. 24D,the connector engine may compare the total scores for all items ofvendor-related data 2410 for one vendor with respect the total scoresfor all items of vendor-related data with other vendors. Based on thecomparison, the connector engine computes a global ranking score 2416 cfor all vendor-related data associated with a subset of the matchedvendor factors 2426 b. For example, the table 2401 a is associated withthe vendor-related data 2411 a corresponding to Vendor A. Using thetotal scores 2431 a, the connector engine calculates rankings 2416 awith respect to the vendor-related data 2411 a. In addition, the table2401 b is associated with the vendor-related data 2411 b correspondingto Vendor B. Using the total scores 2431 b, the connector enginecalculates rankings 2416 b with respect to the vendor-related data 2411a. In one example, the connector engine may create a global rankingtable 2402 by comparing the total score 2431 a from the table 2401 a tothe total score 2431 b from the table 2401 b. Based on the comparison,the connector engine creates a global rank 2416 c of the vendor-relateddata 2411 a-b.

In another embodiment, the connector engine may create a global rankingtable using information associated with an advertisement/marketingsubscription service subscribed to by each of the vendors. For example,the connector engine may add a subscription based tier of ranking to theglobal ranking table 2402. For instance, the connector engine may selecta threshold number (e.g., top ten) of the highest ranked ads from theglobal ranking table 2402. The connector engine may then re-rank theselected ads based on an amount the vendor is willing to pay in responsea user's interaction with the vendor's ads (e.g., pay-per-click). Inanother example, the connector may rank and select the ads associatedwith a vendor based on an amount the vendor is willing to pay inresponse a user's interaction with the vendor's ads (e.g.,pay-per-click).

In an embodiment, a vendor may provide the connector engine with rules.The connector engine uses the rules to select a subset of thevendor-related data. In an example, a vendor may only wish the connectorto select vendor-related data associated with the vendor of the datareceived a score above a certain threshold or a global ranking above acertain threshold.

FIG. 25 is a flow diagram of an example method 2500 for providingvendor-related data. At 2505, the method 2500 begins. At 2510, aconnector engine (e.g., connector engine 2205 of FIG. 22) receivesmodified decision-related data from a decision engine (e.g., decisionengine 2210 of FIG. 22). The received data is associated with a user'sinteraction with the decision engine during a communications sessionbetween the user and the decision engine. The modified decision-relateddata is generated by a decision engine by applying at least one userinput to decision-related data. In particular, the decision-related datais at least one factor used as a criterion in selecting at least onedecision option. The at least one decision option is an output of thedecision engine in response to the application of the at least one userinput to the decision-related data. In an example, the at least onedecision option is product and/or service related data. In anotherexample, the modified decision-related data includes at least one factorhaving a weighted score, where the weighted score of the at least onefactor is determined by at least an importance assigned to the at leastone factor by the user.

At 2515, the connector engine relieves vendor-related data from amarketing database (e.g., marketing database 2235 of FIG. 22) based onone or more properties of the modified decision-related data. Inparticular, the connector engine matches the at least one factor to atleast one vendor factor associated with the vendor-related data andobtains vendor-related data associated with at least a subset of the atleast one vendor factor. The at least one vendor factor is a criterionused in selecting the vendor-related data. In an example, the connectorengine retrieves the vendor-related data by selecting the marketingdatabase corresponding to a vendor and retrieving the vendor-relateddata from the selected at least one marketing database. In anotherexample, the connector engine may select the marketing databasecorresponding to the vendor by selecting the vendor based on whether ornot goods and/or services provided by the vendor correspond to thedecision-related data.

The connector engine, at 2520, processes the modified decision-relateddata to modify the at least one vendor factor. In addition, theconnector engine may rank the vendor-related data by applying weightedscores associated with the decision-related data to the vendor factors.At 2525, the connector engine selects a subset of the vendor-relateddata based on the modified at least one vendor factor. In an example,the connector engine may select the vendor-related data by selecting asubset of the ranked vendor-related data. In another example, theconnector engine may select the subset of ranked vendor-related data byselecting the subset based on an advertisement subscription of a vendorassociated with the vendor-related data. At 2426, the connector enginedetermines if a user's communication session with the decision enginehas terminated or is about to terminate. If the communication session isstill active and the connector engine may repeat the aforementionedactions.

At 2530, if the connector engine determines that the communicationsession has terminated or is about to terminate, the connector enginemay end processing by outputting the selected subset of thevendor-related data based on the modified at least one vendor factor.For instance, the connector engine may i) providing the selected subsetof vendor-related data to the user, ii) trigger a marketing engineassociated with the marketing database to provide the selected subset ofvendor-related data to the user, and/or iii) provide the selected subsetof vendor-related data to a marketing engine associated with themarketing database as a recommendation of vendor-related data to provideto the user, where the marketing engine utilizes the recommendation toprovide vendor-related data to the user.

In another embodiment, the connector engine may also add an additionaltier of ranking that includes factors provided by the connector engineitself. In particular, the connector engine may apply a subscriptionservice factor (e.g., an amount the vendor is willing to pay-per-click).For example, the connector engine may add rank each item of thevendor-related data based on a subscription service elected by thevendor for the specific items (e.g., an amount the vendor is willing topay-per-click).

Further example embodiments of the present disclosure may be configuredusing a computer program product; for example, controls may beprogrammed in software for implementing example embodiments of thepresent disclosure. Further example embodiments of the presentdisclosure may include a non-transitory computer readable mediumcontaining instruction that may be executed by a processor, and, whenexecuted, cause the processor to complete methods described herein. Itshould be understood that elements of the block and flow diagramsdescribed herein may be implemented in software, hardware, firmware, orother similar implementation determined in the future. In addition, theelements of the block and flow diagrams described herein may be combinedor divided in any manner in software, hardware, or firmware. Ifimplemented in software, the software may be written in any languagethat can support the example embodiments disclosed herein. The softwaremay be stored in any form of computer readable medium, such as randomaccess memory (RAM), read only memory (ROM), compact disk read onlymemory (CD-ROM), and so forth. In operation, a general purpose orapplication specific processor loads and executes software in a mannerwell understood in the art. It should be understood further that theblock and flow diagrams may include more or fewer elements, be arrangedor oriented differently, or be represented differently. It should beunderstood that implementation may dictate the block, flow, and/ornetwork diagrams and the number of block and flow diagrams illustratingthe execution of embodiments of the disclosure.

The foregoing description and accompanying figures illustrate theprinciples, preferred embodiments and modes of operation of theinvention. However, the invention should not be construed as beinglimited to the particular embodiments discussed above. Additionalvariations of the embodiments discussed above will be appreciated bythose skilled in the art.

Therefore, the above-described embodiments should be regarded asillustrative rather than restrictive. Accordingly, it should beappreciated that variations to those embodiments can be made by thoseskilled in the art without departing from the scope of the invention asdefined by the following claims.

1-19. (canceled)
 20. A method of comparing a plurality of decisionoptions, said method implemented on a computing device programmable toperform the steps comprising: entering at least one factor; obtaining aplurality of decision options, with each of the plurality of decisionoptions obtained either by determining the decision option based on theat least one factor through a search of a database by an engine or byuser entry; calculating a numeric value for the at least one factorentered based on the at least one factor and the plurality of decisionoptions; calculating a weighted score for each decision factor of eachdecision option; ranking each decision option based on the total scoreof each factor; and outputting the rankings for each decision option.21. The method of claim 20, further comprising entering factor data foreach factor.
 22. The method of claim 20, further comprising entering animportance level for each factor.
 23. The method of claim 22, furthercomprising calculating a weighted score for each factor based on theimportance level for each factor.
 24. The method of claim 23, furthercomprising calculating a total score for each decision option based onthe weighted score of each factor.
 25. The method of claim 20, whereinthe plurality of decision options include at least one of a plurality ofproducts or services.
 26. A computer program embodied on one ofnon-volatile or volatile media comprising at least one code segmentconfigured to instruct a computer to: receive at least one factor;obtain a plurality of decision options, with each of the plurality ofdecision options either determined based on the at least one factorthrough a search of a database by an engine or received through userinput; calculate a numeric value for the at least one factor based onthe at least one factor received and the plurality of decision options;calculate a weighted score for each factor of each of the plurality ofdecision options; rank the plurality of decision options based on thetotal score of each factor; and output the rank of the plurality ofdecision options.
 27. The computer program of claim 26, wherein thecomputer program further comprises at least one code segment configuredto receive an importance level for each factor.
 28. The computer programof claim 27, wherein the computer program further comprises at least onecode segment configured to calculate a weighted score for each factorbased on the importance level of each factor.
 29. The computer programof claim 28, wherein the computer program further comprises at least onecode segment configured to calculate a total score for each decisionoption based on the weighted score of each factor.
 30. The computerprogram of claim 26, wherein the computer program further comprises atleast one code segment configure to receive factor data for each factor.31. The computer program of claim 26, wherein the computer programfurther comprises at least one code segment configured to calculate anumeric value for each of the factors.
 32. A decision making systemcomprising: at least one server coupled in communication with at leastone first party and at least one second party, the at least one serverconfigured to: receive at least one factor; obtain a plurality ofdecision options, each of the plurality of decision options eitherdetermined based on the at least one factor through a search of adatabase by an engine or received through user input; calculate anumeric value for the at least one factor based on the at least onefactor received and the plurality of decision options; calculate aweighted score for each factor of each decision option; rank theplurality of decision options based on the total score of the pluralityof factors of each decision option; and output the rank of the pluralityof decision options.
 33. The system of claim 32, wherein the server isfurther configured to receive factor data for each factor.
 34. Thesystem of claim 32, wherein the server is further configured todetermine one or more decision option that includes at least one productor service.
 35. The system of claim 32, wherein the server is furtherconfigured to receive an importance level of each factor.
 36. The systemof claim 35, wherein the server is further configured to calculate aweighted score for each factor based on the importance level for each ofthe factors.
 37. The system of claim 36, wherein the server is furtherconfigured to calculate a total score for each decision option based onthe weighted score of each factor.